Historically, there has been no way for a supplier to predict, with high certainty, the price at which a product must be sold in order to maximize profits. Under traditional sales models, pricing decisions are made based on estimates, such as anticipated product demand and presumed price sensitivity, in the hope of maximizing profits. The procedure for forming these estimates is time and labor intensive. For example, it is known in existing spreadsheet programs to recalculate derived values automatically from data changes entered into the spreadsheet. Display of such recalculated values facilitates evaluation of hypothetical “what if” scenarios for making business decisions. However, this is done by changing a value in a cell of the spreadsheet, resulting in recalculating all variable entries dependent on the variable changed. It is not easy for the user to see the global effect of such changes without a careful review of the recalculated spreadsheet or separate screens showing graphs derived from the recalculated spreadsheet. The result is a cumbersome iterative process in which the user must change a value in a cell of the spreadsheet, obtain a graph of the resulting dependent variable changes, determine whether those results are as desired, if not, go back to the spreadsheet and make another value change in a cell, redraw the graph, and so forth until desired results are achieved. The process is even more cumbersome if the user desires to add a line to a graph, which requires the generation of new cells in the spreadsheet. An improved system would automatically perform these functions with little input from users.
There are several difficulties in forming an automated dynamic pricing system. One problem is that most sellers keep incomplete pricing data. For example, while the ideal client for the system would maintain data on lost customers, competitor prices, industry availability and the like, most sellers will have data on only a subset of the potential drivers of market response. Furthermore, the known dynamic pricing system can neither adjust rapidly to account for changes in market conditions nor suggest different prices for different markets.